fix(cost): apply the regional-processing uplift to gpt-image spend

completion_cost already received data_residency but never forwarded it
into the image branch, so deployments with data_residency: eu never
applied the +10% uplift to image-generation spend. The residency now
flows through route_image_generation_cost_calculator into the OpenAI
gpt-image calculator and its usage-based helpers, and the post-2026-03-05
gpt-image models carry the eu/us uplift multipliers like gpt-6 does.
This commit is contained in:
OrangerXu 2026-10-14 17:34:53 +08:00
parent b22a0bd9e5
commit f594efe634
6 changed files with 194 additions and 15 deletions

View file

@ -1660,6 +1660,7 @@ def completion_cost(
call_type=call_type,
model_info=_deployment_model_info(litellm_logging_obj, custom_pricing, router_model_id),
vertex_location=vertex_location,
data_residency=data_residency,
)
elif call_type in _VIDEO_CALL_TYPES:
### VIDEO GENERATION COST CALCULATION ###

View file

@ -1805,6 +1805,7 @@ def calculate_image_response_cost_from_usage(
custom_llm_provider: str,
model_info: ModelInfo | None = None,
vertex_location: str | None = None,
data_residency: str | None = None,
) -> float | None:
"""
Calculate image generation cost from usage metadata when available.
@ -1889,6 +1890,7 @@ def calculate_image_response_cost_from_usage(
custom_llm_provider=custom_llm_provider,
model_info=model_info,
vertex_location=vertex_location,
data_residency=data_residency,
)
return prompt_cost + completion_cost
@ -1953,6 +1955,7 @@ class CostCalculatorUtils:
call_type: str | None = None,
model_info: ModelInfo | None = None,
vertex_location: str | None = None,
data_residency: str | None = None,
) -> float:
"""
Route the image generation cost calculator based on the custom_llm_provider
@ -2104,6 +2107,7 @@ class CostCalculatorUtils:
image_response=completion_response,
custom_llm_provider=custom_llm_provider,
model_info=pricing,
data_residency=data_residency,
)
# Fall through to default for DALL-E models
return default_image_cost_calculator(

View file

@ -21,6 +21,7 @@ def cost_calculator(
image_response: ImageResponse,
custom_llm_provider: str | None = None,
model_info: ModelInfo | None = None,
data_residency: str | None = None,
) -> float:
"""Calculate cost for OpenAI gpt-image models (token-based pricing)."""
provider: Final = custom_llm_provider or "openai"
@ -38,7 +39,11 @@ def cost_calculator(
# A chat Usage with an explicit output breakdown: cost via generic_cost_per_token.
if isinstance(usage, Usage) and usage.completion_tokens_details is not None:
prompt_cost, completion_cost = generic_cost_per_token(
model=model, usage=usage, custom_llm_provider=provider, model_info=price_table
model=model,
usage=usage,
custom_llm_provider=provider,
model_info=price_table,
data_residency=data_residency,
)
return prompt_cost + completion_cost
@ -48,7 +53,11 @@ def cost_calculator(
# does not itemize output and splitting text/image when it does.
if getattr(usage, "input_tokens", None) is not None:
token_based_cost: Final = calculate_image_response_cost_from_usage(
model=model, image_response=image_response, custom_llm_provider=provider, model_info=price_table
model=model,
image_response=image_response,
custom_llm_provider=provider,
model_info=price_table,
data_residency=data_residency,
)
if token_based_cost is not None:
return token_based_cost
@ -57,7 +66,11 @@ def cost_calculator(
# cost via generic_cost_per_token (text rate) instead of returning 0.0.
if isinstance(usage, Usage):
prompt_cost, completion_cost = generic_cost_per_token(
model=model, usage=usage, custom_llm_provider=provider, model_info=price_table
model=model,
usage=usage,
custom_llm_provider=provider,
model_info=price_table,
data_residency=data_residency,
)
return prompt_cost + completion_cost

View file

@ -34044,7 +34044,9 @@
"/v1/images/edits"
],
"supports_vision": true,
"supports_pdf_input": true
"supports_pdf_input": true,
"regional_processing_uplift_multiplier_eu": 1.1,
"regional_processing_uplift_multiplier_us": 1.1
},
"gpt-image-2-2026-04-21": {
"cache_read_input_image_token_cost": 2e-06,
@ -34060,7 +34062,9 @@
"/v1/images/edits"
],
"supports_vision": true,
"supports_pdf_input": true
"supports_pdf_input": true,
"regional_processing_uplift_multiplier_eu": 1.1,
"regional_processing_uplift_multiplier_us": 1.1
},
"gpt-image-2.5-flare": {
"cache_read_input_image_token_cost": 2e-06,
@ -34079,7 +34083,9 @@
],
"supports_vision": true,
"supports_pdf_input": true,
"source": "https://developers.openai.com/api/docs/pricing"
"source": "https://developers.openai.com/api/docs/pricing",
"regional_processing_uplift_multiplier_eu": 1.1,
"regional_processing_uplift_multiplier_us": 1.1
},
"gpt-image-2.5-flare-2026-09-08": {
"cache_read_input_image_token_cost": 2e-06,
@ -34095,7 +34101,9 @@
],
"supports_vision": true,
"supports_pdf_input": true,
"source": "https://developers.openai.com/api/docs/pricing"
"source": "https://developers.openai.com/api/docs/pricing",
"regional_processing_uplift_multiplier_eu": 1.1,
"regional_processing_uplift_multiplier_us": 1.1
},
"gpt-image-2.5-sunburst": {
"cache_read_input_image_token_cost": 2e-06,
@ -34114,7 +34122,9 @@
],
"supports_vision": true,
"supports_pdf_input": true,
"source": "https://developers.openai.com/api/docs/pricing"
"source": "https://developers.openai.com/api/docs/pricing",
"regional_processing_uplift_multiplier_eu": 1.1,
"regional_processing_uplift_multiplier_us": 1.1
},
"gpt-image-2.5-sunburst-2026-09-08": {
"cache_read_input_image_token_cost": 2e-06,
@ -34130,7 +34140,9 @@
],
"supports_vision": true,
"supports_pdf_input": true,
"source": "https://developers.openai.com/api/docs/pricing"
"source": "https://developers.openai.com/api/docs/pricing",
"regional_processing_uplift_multiplier_eu": 1.1,
"regional_processing_uplift_multiplier_us": 1.1
},
"low/1024-x-1024/gpt-image-1.5": {
"deprecation_date": "2026-12-01",

View file

@ -34044,7 +34044,9 @@
"/v1/images/edits"
],
"supports_vision": true,
"supports_pdf_input": true
"supports_pdf_input": true,
"regional_processing_uplift_multiplier_eu": 1.1,
"regional_processing_uplift_multiplier_us": 1.1
},
"gpt-image-2-2026-04-21": {
"cache_read_input_image_token_cost": 2e-06,
@ -34060,7 +34062,9 @@
"/v1/images/edits"
],
"supports_vision": true,
"supports_pdf_input": true
"supports_pdf_input": true,
"regional_processing_uplift_multiplier_eu": 1.1,
"regional_processing_uplift_multiplier_us": 1.1
},
"gpt-image-2.5-flare": {
"cache_read_input_image_token_cost": 2e-06,
@ -34079,7 +34083,9 @@
],
"supports_vision": true,
"supports_pdf_input": true,
"source": "https://developers.openai.com/api/docs/pricing"
"source": "https://developers.openai.com/api/docs/pricing",
"regional_processing_uplift_multiplier_eu": 1.1,
"regional_processing_uplift_multiplier_us": 1.1
},
"gpt-image-2.5-flare-2026-09-08": {
"cache_read_input_image_token_cost": 2e-06,
@ -34095,7 +34101,9 @@
],
"supports_vision": true,
"supports_pdf_input": true,
"source": "https://developers.openai.com/api/docs/pricing"
"source": "https://developers.openai.com/api/docs/pricing",
"regional_processing_uplift_multiplier_eu": 1.1,
"regional_processing_uplift_multiplier_us": 1.1
},
"gpt-image-2.5-sunburst": {
"cache_read_input_image_token_cost": 2e-06,
@ -34114,7 +34122,9 @@
],
"supports_vision": true,
"supports_pdf_input": true,
"source": "https://developers.openai.com/api/docs/pricing"
"source": "https://developers.openai.com/api/docs/pricing",
"regional_processing_uplift_multiplier_eu": 1.1,
"regional_processing_uplift_multiplier_us": 1.1
},
"gpt-image-2.5-sunburst-2026-09-08": {
"cache_read_input_image_token_cost": 2e-06,
@ -34130,7 +34140,9 @@
],
"supports_vision": true,
"supports_pdf_input": true,
"source": "https://developers.openai.com/api/docs/pricing"
"source": "https://developers.openai.com/api/docs/pricing",
"regional_processing_uplift_multiplier_eu": 1.1,
"regional_processing_uplift_multiplier_us": 1.1
},
"low/1024-x-1024/gpt-image-1.5": {
"deprecation_date": "2026-12-01",

View file

@ -0,0 +1,137 @@
"""
Tests for the OpenAI regional-processing uplift on gpt-image spend
This tests the fix for GitHub issue #46407:
https://github.com/BerriAI/litellm/issues/46407
`data_residency: eu` (routed through eu.api.openai.com) must apply the
per-model regional uplift multiplier to image-generation spend exactly as it
already does for chat/responses spend.
"""
import pytest
import litellm
from litellm.types.utils import (
ImageObject,
ImageResponse,
ImageUsage,
ImageUsageInputTokensDetails,
)
@pytest.fixture(autouse=True)
def _use_local_model_cost_map(monkeypatch):
original_model_cost = litellm.model_cost
monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
litellm.model_cost = litellm.get_model_cost_map(url="")
litellm.get_model_info.cache_clear()
try:
yield
finally:
litellm.model_cost = original_model_cost
litellm.get_model_info.cache_clear()
def _usage() -> ImageUsage:
return ImageUsage(
input_tokens=100,
input_tokens_details=ImageUsageInputTokensDetails(image_tokens=50, text_tokens=50),
output_tokens=200,
total_tokens=300,
)
def _image_response() -> ImageResponse:
return ImageResponse(
created=1234567890,
data=[ImageObject(url="http://example.com/image.jpg")],
usage=_usage(),
)
class TestImageDataResidencyUplift:
def test_image_usage_path_applies_eu_uplift(self) -> None:
"""The ImageUsage path (calculate_image_response_cost_from_usage) must apply the EU multiplier."""
from litellm.llms.openai.image_generation.cost_calculator import cost_calculator
image_response = _image_response()
base_cost = cost_calculator(
model="gpt-image-2.5-flare",
image_response=image_response,
custom_llm_provider="openai",
)
eu_cost = cost_calculator(
model="gpt-image-2.5-flare",
image_response=image_response,
custom_llm_provider="openai",
data_residency="eu",
)
assert base_cost > 0
assert eu_cost == pytest.approx(base_cost * 1.1)
def test_route_from_completion_cost_applies_eu_uplift(self) -> None:
"""completion_cost forwards data_residency into the image branch (issue #46407 part 1)."""
base_cost = litellm.completion_cost(
model="gpt-image-2.5-flare",
custom_llm_provider="openai",
completion_response=_image_response(),
call_type="aimage_generation",
)
eu_cost = litellm.completion_cost(
model="gpt-image-2.5-flare",
custom_llm_provider="openai",
completion_response=_image_response(),
call_type="aimage_generation",
data_residency="eu",
)
assert base_cost > 0
assert eu_cost == pytest.approx(base_cost * 1.1)
def test_us_uplift_applies_and_unknown_residency_does_not(self) -> None:
from litellm.llms.openai.image_generation.cost_calculator import cost_calculator
image_response = _image_response()
base_cost = cost_calculator(
model="gpt-image-2.5-flare",
image_response=image_response,
custom_llm_provider="openai",
)
us_cost = cost_calculator(
model="gpt-image-2.5-flare",
image_response=image_response,
custom_llm_provider="openai",
data_residency="us",
)
unknown_cost = cost_calculator(
model="gpt-image-2.5-flare",
image_response=image_response,
custom_llm_provider="openai",
data_residency="ap-southeast-1",
)
assert us_cost == pytest.approx(base_cost * 1.1)
assert unknown_cost == pytest.approx(base_cost)
def test_pre_cutoff_gpt_image_model_has_no_uplift(self) -> None:
"""gpt-image-1.5 predates the 2026-03-05 cutoff and carries no multiplier: residency must not change spend."""
from litellm.llms.openai.image_generation.cost_calculator import cost_calculator
image_response = _image_response()
base_cost = cost_calculator(
model="gpt-image-1.5",
image_response=image_response,
custom_llm_provider="openai",
)
eu_cost = cost_calculator(
model="gpt-image-1.5",
image_response=image_response,
custom_llm_provider="openai",
data_residency="eu",
)
assert base_cost > 0
assert eu_cost == pytest.approx(base_cost)